본 연구는 교육부에서 주관하고(법무부와 연계) 한국연구재단이 운영하는 교육국제화역량 인증제(IEQAS) 4주기 평가지표를 대상으로, 지표 체계가 고등교육기관의 외국인 유학생 유치·관리 역량을 타당하게 검증하는지 진단하고 개선 방향을 제시한다. 분석틀은 국제화(Knight), 품질 개념(Harvey & Green), 정책평가·형평성(Dunn), 구조–과정–성과(Donabedian)의 논 의를 토대로 품질보증(QA), 형평성(수평/수직), 성과사슬(구조–과정–성과), 행정부담·표준화의 네 관점으로 구성하였다. 연구 방법은 질적 문서분석 으로, 2011년 시범도입 이후 주기별 편람과 2025년 4주기 IEQAS 편람, 정책문서·선행연구, 2023년부터 2025년까지 언론 보도를 검토하였다. 분 석 결과, 4주기 IEQAS 평가는 산식 합리화와 고등직업교육 트랙 신설 등 개선이 있었으나 지역·유형 격차 반영의 한계, 성과 지표 편중, 지표 별 기준 시점·집계기간 불일치, 어학연수 교원·고용구조 평가 공백이 확 인되었다. 이에 수도권/비수도권 분리 평가, 단계형 사후관리, 온라인·혼 합수업의 과정 품질지표 보강, 기준시점·집계기간·산식 표준화, 어학연수 교원·고용지표 도입을 제안한다. 이를 통해 IEQAS가 국제화·유학생 정책 의 핵심 품질보증 인프라로 기능하도록 개선 방향을 제시하고자 한다.

Grease consistency is a critical quality factor in industrial lubrication processes, as it significantly affects mechanical performance, operational stability, and product durability. In grease manufacturing, fluctuations in process variables such as feed temperature, evaporation time, flow rate, and environmental conditions can cause inconsistencies in quality, which may lead to operational defects or increased maintenance costs. To address this challenge, this study proposes a predictive modeling approach for forecasting grease consistency with the aim of enhancing process quality. Real manufacturing process data were collected from a grease production facility, and irrelevant or highly correlated variables were eliminated through multicollinearity analysis and dimensionality reduction. Multiple machine learning regression techniques were applied and evaluated to identify the most effective model for predicting grease consistency. Through systematic comparison, the final predictive model was developed to provide accurate consistency estimation based on selected process variables. The proposed model enables proactive quality control by allowing consistency deviations to be detected early, thereby supporting process optimization and decision-making in manufacturing environments. This research demonstrates the applicability of data-driven predictive modeling in the grease industry and contributes to the development of intelligent quality management strategies in modern manufacturing. The findings suggest that machine learning-based consistency prediction can play a key role in improving production efficiency and ensuring stable product performance.

This study quantitatively evaluated the real-world performance of an IoTbased, context-aware mobile air purification system. Additionally, this system is proposed as a practical alternative to conventional stationary purifiers, overcoming their spatial limitations. To analyze concentration variations, removal efficiency, and air cleaning ratio (ACR) for PM2.5, PM10, and HCHO, three scenarios were tested: S1 (natural ventilation), S2 (stationary purifier), and S3 (IoT-based mobile air purification system). The mobile system (S3) achieved a 1.6-fold higher removal efficiency for PM2.5 compared with the stationary purifier (S2) and reduced the ACR to below 0.4 within 30 minutes after high-concentration events. In contrast, stationary purifiers required approximately 333 minutes to reach background levels (17.11 μg/m3), revealing about a 10-fold difference in cleaning speed. Monte Carlo simulations confirmed the consistent superiority of S3 for both particulate and gaseous pollutants, with HCHO concentrations 36.7% lower (90th percentile) than under S2. According to the health risk assessment, the asthma hospitalization rate decreased by over 40%, the HQ for PM2.5 decreased from 1.1 (S1) to 0.64 (S3), and the ECR for HCHO was 0.62 times that of S2. These findings highlight that spatial responsiveness and mobility, along with filter capacity, are key determinants of air purification performance. In conclusion, the mobile air purifier effectively overcomes the structural constraints of stationary devices and establishes a new paradigm for realtime, adaptive indoor air quality management that helps safeguard occupant health.

As renewable energy penetration continues to increase, the output variability and forecasting uncertainty of photovoltaic generation have emerged as major operational risks in power systems. This study establishes a sensor-based data quality control procedure to ensure the reliability of meteorological data collected at a PV plant. For temperature, humidity, and wind speed, a four stage QC process physical range check, persistence check, step change check, and median filtering was applied. Solar radiation, which exhibits strong temporal and distributional characteristics, was processed using a three-stage QC procedure consisting of physical range, step change, and frequency distribution checks. Using the quality-controlled meteorological data, PV generation forecasting was performed with SVM and XGBoost models. As a result, the MAPE values improved to 6.32% for SVM and 6.08% for XGBoost after QC application. The findings confirm that meteorological data quality control significantly enhances PV forecasting accuracy and can support future strategies for distributed energy resource management, curtailment mitigation, and power system risk reduction.

Purpose: This study applied a mixed-methods design to examine the effects of the nurse shift system improvement pilot project, organized by the Ministry of Health and Welfare, on ward nurses’ quality of life, job satisfaction, and patient safety nursing activities, as well as to explore their overall evaluation of the project through in-depth interviews. Methods: Using a mixed-methods approach, this study simultaneously conducted quantitative analyses of ward nurses’ quality of life, job satisfaction, and patient safety nursing activities, along with qualitative research through in-depth interviews to explore overall perceptions of the nurse shift system improvement pilot project. Results: Patient safety nursing activities showed a significant positive correlations with quality of life (r=.24, p=.017) and job satisfaction (r=.21, p=.038). There was also a significant positive correlation between quality of life and job satisfaction (r=.63, p<.001). As a result of the qualitative analysis, eight facilitating factors and five hindering factors related to the pilot project were identified. Conclusion: Through the research, the effectiveness of the pilot project and the factors influencing nurses were identified. Accordingly, strategies are needed to enhance patient safety nursing activities by improving nurses’ quality of life and job satisfaction in pilot project wards.

Indoor air quality is a critical factor affecting health and quality of life, especially in spaces frequently used by sensitive populations such as adolescents. This study assessed the impact of garden ball installations and electrochemical fertilizer applications on indoor air quality in two youth centers, Center S and Center W, located in Bucheon, South Korea. PM2.5, PM10, and CO2 concentrations were monitored and analyzed based on the presence of garden balls and the use of electrochemical fertilizers. The results showed that spaces with garden balls (w/ G.B.) had significantly lower PM2.5 and PM10 concentrations compared to offices and spaces without garden balls (w/o G.B.). In Center W, the presence of garden balls alone improved air quality, highlighting the potential of vertical greening as a sustainable solution. In Center S, the application of electrochemical fertilizers during the “after + period” (when both garden balls and electrochemical fertilizers were applied) further enhanced particulate matter reduction, demonstrating the fertilizers’ ability to amplify plants’ air-purifying effects. This study provides empirical evidence that garden balls are an eco-friendly option for indoor air quality management. Combining electrochemical fertilizers with garden balls shows promise for enhancing air quality, offering a practical model for multi-use facilities such as youth centers.

This study aims to propose new grading standards that can be applied to AI-based automatic sorting machines, reflecting current distribution and consumption trends. The current domestic grading standards for onions in South Korea are based on the “Agricultural and Fishery Products Quality Control Act”. They classify onions based on criteria such as uniformity, shape, color, and the presence of foreign matter. Onion grading standards are divided into four categories based on bulb diameter and weight. However, in the actual domestic market, onions are distributed according to a five-grade classification based on bulb diameter. Therefore, this study classified onions into eight grades, reflecting current distribution and consumption trends in the domestic market. These grades are applicable to AI-based automatic sorting machines. Marketable onions were classified into A1 (extra large) to A5 (extra small) based on the diameter of a single bulb. Onions used for non-marketable purposes (processing) were classified as grade B. Additionally, grade C and grade D were designated for processing and disposal, respectively. By establishing quality grading classifications that align with current distribution and consumption market trends as well as the operational characteristics of AI-based automatic sorting machines, we can expect improvements in work efficiency and reductions in distribution costs. Following this study, it will be necessary to establish comprehensive quality grading standards that include both external criteria (such as bulb weight and size) and internal criteria (such as detection of internal decay and disease occurrence).

In contemporary society, consumers increasingly demand foods that provide health benefits and satisfy taste. As a result, the importance of functional foods has risen, particularly in the development of functional bread using various ingredients. This study examines the impact of different functional ingredients on bread quality, including plant-based components like vegetables, fruits, and grains, as well as animal-derived materials, medicinal herbs, and other additives. The study evaluates changes in the physical, chemical, and sensory properties of bread dough. Plant-based ingredients improved the color, flavor, and texture of the bread, enhancing its overall sensory quality. They also increased water absorption in the dough, improving its appearance and texture. Animal-derived ingredients, especially from insects, enhanced the physical properties and nutritional value of the dough. Medicinal herbs significantly boosted the bread's nutritional value and health benefits through antioxidant and anti-inflammatory properties. The findings show that functional additives play a crucial role in improving bread quality, providing valuable data for the development of functional foods that meet modern consumer demands for health benefits and good taste.

Purpose: This case report aimed to provide basic data to improve instrumental activities of daily living and quality of care for elderly with dementia. Methods: A total of ten sessions were organized and operated once a week for 16 elderly with dementia. Data analysis was conducted through observational evaluation of participation, concentration, and satisfaction, and pre and post test for each individual. In-depth interviews were performed at the end of each session. Results: Participants were 16.0% in their 70s, 68.7% in their 80s, and 6.3% in their 90s. Their average instrumental activities daily living (I-ADL) score was 20.12±4.93. Post test scores increased in 78% of the elderly. Participation, concentration, and satisfaction were the highest in the 8th session, which was operated under the theme of ‘A good day to go to the hospital,’ and the lowest participation, concentration, and satisfaction were in the 5th session, which was operated under the theme of ‘A good day to go out.’ Conclusion: In the case of elderly with mild or early dementia, performance of instrumental activities of daily living can be improved through repetitive learning. Thus, there is a need to continuously provide such program as a regular program for elderly people in the early stages of dementia.

This research investigated the preparation of activated carbon derived from Krabok (Irvingia malayana) seed shells to improve the quality of crude glycerol obtained during biodiesel production. The activated carbon was prepared using a dry chemical activation method with NaOH, utilizing an innovative biomass incinerator. The results revealed that the resulting KC/AC-two-step exhibited favorable physicochemical adsorption properties, with a high surface area of 758.72 m2/g and an iodine number of 611.10 mg/g. These values meet the criteria of the industrial product standard for activated carbon No. TIS 900-2004, as specified by the Ministry of Industry in Thailand. Additionally, the adsorption efficiency for methylene blue reached an impressive 99.35 %. This developed activated carbon was then used to improve the quality of crude glycerol obtained from biodiesel production. The experimental results showed that the KC/AC-two-step increased the purity of crude glycerol to 73.61 %. In comparison, commercially available activated carbon (C/AC) resulted in a higher crude glycerol purity of 81.19 %, as analyzed by the GC technique. Additionally, the metal content (Zn, Cu, Fe, Pb, Cd, and Na) in purified glycerol using KC/AC-two-step was below the standards for heavy metals permitted in food and cosmeceuticals by the Food and Drug Administration of Thailand and the European Committee for Food Contact Materials and Articles. As a result, it can be inferred that Krabok seed shells have favorable properties for producing activated carbon suitable as an adsorbent to enhance crude glycerol purity. Furthermore, the improved crude glycerol from this research has potential for various industrial applications.

This study examined the impact of two bacterial strains, H05E-12 and H05R- 04, on alleviating non-irrigation-induced stress and its subsequent effects on the fruit productivity of sweet pumpkin plants. When subjected to non-irrigation-induced stress, the lipid peroxidation, proline, total phenol, and total soluble sugar content significantly decreased in plants treated with either H05E-12 or H05R-04 compared to the control. In a greenhouse experiment under non-irrigated conditions, H05E-12-treated plants exhibited higher stomatal conductance than the control, although there was no significant change in the soil plant analysis development (SPAD) value due to treatment. Upon re-watering, an increase in fruit diameter was observed in H05E-12-treated plants, and the L-ascorbic acid content in the fruit also showed a significant increase compared to the control. The H05E-12 strain was identified as Kushneria konosiri. To the best of our knowledge, this is the first report detailing the beneficial effects of K. konosiri on the alleviation of non-irrigation-induced stress and the promotion of plant growth in sweet pumpkin plants.

Purpose: This case aims to present basic data needed to improve cognitive function and quality of care for aged with dementia. Methods: A total of ten sessions were organized and operated once a week for 12 aged with dementia using the center. Data collection was conducted from July 27 to September 30, 2022. Data analysis was conducted through observational evaluation of participation, satisfaction, and performance of each individual and in-depth interviews at the end of the program. Results: Among the 12 subjects who participated in the quality improvement program, an average of 8.4 subjects in each session showed high levels of participation and satisfaction. On the other hand, the performance level was low, with an average of 6.9 subjects per session. In the in-depth interview, two concepts were confirmed: ‘regrets about the past’ and ‘satisfaction with present life.’ Conclusions: We hope that the results of this case report will be useful in developing a multifaceted cognitive therapy program to improve the cognitive function of aged with dementia in the future.

In this study, we focus on the improvement of data quality transmitted from a weather buoy that guides a route of ships. The buoy has an Internet-of-Thing (IoT) including sensors to collect meteorological data and the buoy’s status, and it also has a wireless communication device to send them to the central database in a ground control center and ships nearby. The time interval of data collected by the sensor is irregular, and fault data is often detected. Therefore, this study provides a framework to improve data quality using machine learning models. The normal data pattern is trained by machine learning models, and the trained models detect the fault data from the collected data set of the sensor and adjust them. For determining fault data, interquartile range (IQR) removes the value outside the outlier, and an NGBoost algorithm removes the data above the upper bound and below the lower bound. The removed data is interpolated using NGBoost or long-short term memory (LSTM) algorithm. The performance of the suggested process is evaluated by actual weather buoy data from Korea to improve the quality of ‘AIR_TEMPERATURE’ data by using other data from the same buoy. The performance of our proposed framework has been validated through computational experiments based on real-world data, confirming its suitability for practical applications in real- world scenarios.

탁주의 제조과정 중 사입량의 비율을 조절하여 담금 조건을 달리하는 증량 담금법과 3단 담금법으로 탁주를 제조하여 탁주의 품질특성을 조사하였 다. 3단 담금법에서 초기 당도가 높았으나, 발효가 진행됨에 따라 발효 6일차 까지는 10.5oBrix로 서서히 감소하다가, 그 이후 다시 상승하였다. 증량 담금법의 알코올 증가량은 3단 담금법에 비해 초기 발효속도는 완만했으나 발효완료 시 16.0%에 도달한 반면, 3단 담금법은 초기 발효속도가 빨랐으나, 14.9%로 발효가 완료되었다. 초기 아미노산도는 3단 담금법에서는 2.3%로 높았으나, 증량 담금법은 아미노산도가 서서히 증가하여 발효완료시 3.2%였고, 3단 담금법에서는 3.3%에 도달하였다. 증량 담금법에서 Sake meter value (SMV)는 발효 3일차에 -13.1까지 감소하였으나, 그 이후 지속적으로 증가하여 발효 종료시 11.3으로 나타났다. 관능검사 결과 전반적 기호도는 단맛과 쓴맛의 기호도가 우수했던 증량 담금법에서 더 높게 나타났다.

This study was conducted to evaluate quality and anthocyanin content of ‘Tarocco’ orange fruit in order to support early harvest. Harvest times were set at 280 days after anthesis (DAA), 300 DAA, and 320 DAA (conventional harvest). The fruits were classified as those having an intense reddish color or a yellowish orange color in the rind. The fruits were stored during 45 days at 8oC, for low temperature storage, or 18oC, for room temperature storage in winter. The changes in the fruit quality were measured at 15-days interval. The reddish rind fruit had slightly higher sugar content than the orange fruits with yellowish rind, but had similar acidity. A sugar content of 13.0 oBrix or more was recorded for fruits harvested 280 DAA with 45 days of low-temperature storage. A similar finding was recorded after 15 days of low temperature storage for the fruits harvested 300 DAA with 30 days for the conventional harvest. The anthocyanin content was higher for fruits with later harvest time (37.8 mg·L-1) and longer storage period in both low and winter room temperatures (25.2-53 mg·L-1 and 10.3-51.9 mg·L-1, respectively). For all harvest periods, the peel and juice color intensity increased after 15 days of low temperature storage and remained constant regardless of the storage temperature. The result indicated that the fruit quality and anthocyanin content of fruits harvested 300 DAA was better than fruits harvested 320 DAA (conventional harvest), stored 15 days at 8oC or 30 days at 18oC after harvesting 300 DAA.

지구온난화 완화를 위한 노력이 전 세계적으로 계속되고 있는 가운데 우리나라는 2030년 온실가스 배출량을 Business As Usual (BAU) 대비 37% 감축하는 목표를 설정하고 이산화탄소 순 배출 제로 달성을 목표로 하는 ‘2050탄소중립 시나리오’를 발표하였다. 이러한 상황에서 케나프 (Kenaf)는 높은 탄소흡수율과 빠른 성장으로 인해 대체 해결방안으로 제안된다. 시료선정에 대한 분석결과, 케나프 잎은 질소 함량이 높아 적합하지 않은 것으로 나타났고, 줄기와 잎-줄기 혼합 케나프는 Biomass-Solid Refuse Fuel (Bio-SRF) 등급을 충족했다. Bio-SRF 등급을 만족하였지만, Bio-SRF 등급으로 이용할 수 있는 활용처가 뚜렷하지 않다. 따라서 케나프의 연료품질을 반탄화 공정을 통해 개선하여 보다 효율적으로 활용하고자 한다. 반탄화 공정의 공시재료로 줄기만 사용하는 것보다 시료 이용률이 높은 잎-줄기 혼합 케나프를 선택하였다. 반탄화 공정은 여섯가지의 공정 온도 (200·210·220·230·240·250℃)와 다른 세 가지 공정 시간(20·30·40 min)에서 수행되었다. 반탄화 공정결과, 공정시간이 길수록 230℃, 240℃, 250℃ 온도에서 질량감소가 크기 때문에 에너지수율이 낮았다. 따라서 본 연구에서는 200℃·20 min, 200℃·30 min, 210℃·20 min, 220℃·20 min의 에너지 수율이 높고 질량감소가 작아 최적 반탄화 공정 조건으로 선정하였다.